Real world challenges in defining oligometastatic disease in clinical practice.
Bibliographic record
Abstract
e18873 Background: There is little data understanding the multi-disciplinary application of oligo-metastatic disease (OMD) treatment and decision-making. Through an anonymous survey, we sought to understand the knowledge gaps and challenges faced by physicians caring for cancer patients in deciphering and delivering treatments for patients with OMD. Methods: This was an IRB approved single institution quality improvement study conducted via an anonymous electronic survey. Three clinical cases of OMD that ranged from de-novo OMD to oligo-progressive disease, were presented to check participants’ comprehension of OMD. Descriptive statistics were used to summarize quantifiable information obtained from the survey. A qualitative approach was taken for the open-ended questions, in which the answers were reviewed by 2 independent readers and grouped together into common themes, and analyzed using sector and bar diagram, decision-tree method and sorted by prevalence. Results: The survey was answered by 70 clinicians (39 (56%) medical oncologists, 17 (24%) radiation oncologists, 5 (7%) surgeons, 9 (13%) from anatomical pathology/radiology/palliative care). The three clinical cases were correctly answered in 63%, 94% and 76%, respectively; of these, 76% to 84% would offer local treatment for each OMD scenario. Most (79%) perceived differences between local therapies (surgery, SBRT and RFA). Surgery was preferred to improve local control and overall survival, while SBRT was considered as being less invasive and more beneficial to patient quality of life. The definition of OMD was perceived by 94% as patients harboring 1-5 metastases. The main perceived challenges consist of lack of evidence in clinical and prospective trial data. Referrals are hindered as the goals and approach of OMD care are unclear. The most important determinant in deciding whether patients may benefit from OMD treatment is tumor histology and molecular profile. Conclusions: SBRT as a treatment of OMD emerged during an era of rapid expansion in systemic treatments and improvements in imaging techniques. Positive and negative trials in various histologies of cancer further added uncertainty on who would best benefit from OMD SBRT. As more radiation centres offer SBRT, the discordance in the outcome expectations from referring physicians, radiation oncologists and patients will need to be addressed to ensure that patients’ goals of care are met.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".